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Peer-reviewedCC BYFrontiers in bioinformatics

Characterizing and mitigating protocol-dependent gene expression bias in 3' and 5' single-cell RNA sequencing.

  • Valeryia Shydlouskaya1,2
  • S M Mansour Haeryfar1,2,3,4,5
  • Tallulah S Andrews2,3,6,7
7 affiliations
  1. 1Department of Microbiology and Immunology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  2. 2Collaborative Specialization in Machine Learning in Health and Biomedical Sciences, Western University, London, ON, Canada.
  3. 3Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  4. 4Department of Medicine, Division of Clinical Immunology & Allergy, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  5. 5Department of Surgery, Division of General Surgery, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  6. 6Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  7. 7Department of Computer Science, Western University, London, ON, Canada.
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Abstract

Single-cell RNA sequencing (scRNA-seq) has enabled large-scale characterization of cellular heterogeneity; yet, integrating datasets generated through different library preparation protocols remains challenging. For instance, comparisons between 10X Genomics 3' and 5' chemistries are complicated by protocol-dependent technical biases imposed by differences in transcript end capture and amplification. While normalization, and often batch correction, is an integral step in preprocessing scRNA-seq datasets, it remains unclear which correction is most appropriate, or even necessary, for reliable cross-protocol comparisons. Here, we systematically characterize protocol-related expression differences using matched 3' and 5' scRNA-seq data from 36 subjects across six datasets spanning multiple tissues. We find that gene expression discrepancies are not pervasive across the whole transcriptome but are instead driven by a relatively small, reproducible subset of protocol-biased genes. These protocol-specific differences were not primarily explained by GC content or gene length. Excluding the affected genes improves cross-protocol concordance, indicating that most genes are directly comparable without aggressive correction. We then benchmark eleven commonly employed normalization approaches and show that while several methods, such as fastMNN, improve statistical alignment when cell populations are well matched, they can distort gene-level signals and inflate differential expression in biologically realistic settings with incomplete cell-type overlap. Taken together, our results demonstrate that protocol bias between 3' and 5' scRNA-seq is limited in scope and that targeted handling of biased genes presents an alternative approach to normalization or batch correction strategies when integrating and comparing data across protocols.

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Cite

BibTeXRIS
BibTeX
@article{shydlouskaya2026characterizing,
  title = {{Characterizing and mitigating protocol-dependent gene expression bias in 3' and 5' single-cell RNA sequencing.}},
  author = {Valeryia Shydlouskaya and S M Mansour Haeryfar and Tallulah S Andrews},
  journal = {Frontiers in bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.3389/fbinf.2026.1931243},
  url = {https://europepmc.org/article/MED/42923213},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Characterizing and mitigating protocol-dependent gene expression bias in 3' and 5' single-cell RNA sequencing.
AU  - Valeryia Shydlouskaya
AU  - S M Mansour Haeryfar
AU  - Tallulah S Andrews
PY  - 2026
DA  - 2026/10/08
JO  - Frontiers in bioinformatics
DO  - 10.3389/fbinf.2026.1931243
UR  - https://europepmc.org/article/MED/42923213
AB  - Single-cell RNA sequencing (scRNA-seq) has enabled large-scale characterization of cellular heterogeneity; yet, integrating datasets generated through different library preparation protocols remains challenging. For instance, comparisons between 10X Genomics 3' and 5' chemistries are complicated by protocol-dependent technical biases imposed by differences in transcript end capture and amplification. While normalization, and often batch correction, is an integral step in preprocessing scRNA-seq datasets, it remains unclear which correction is most appropriate, or even necessary, for reliable cross-protocol comparisons. Here, we systematically characterize protocol-related expression differences using matched 3' and 5' scRNA-seq data from 36 subjects across six datasets spanning multiple tissues. We find that gene expression discrepancies are not pervasive across the whole transcriptome but are instead driven by a relatively small, reproducible subset of protocol-biased genes. These protocol-specific differences were not primarily explained by GC content or gene length. Excluding the affected genes improves cross-protocol concordance, indicating that most genes are directly comparable without aggressive correction. We then benchmark eleven commonly employed normalization approaches and show that while several methods, such as fastMNN, improve statistical alignment when cell populations are well matched, they can distort gene-level signals and inflate differential expression in biologically realistic settings with incomplete cell-type overlap. Taken together, our results demonstrate that protocol bias between 3' and 5' scRNA-seq is limited in scope and that targeted handling of biased genes presents an alternative approach to normalization or batch correction strategies when integrating and comparing data across protocols.
SN  - 2673-7647
ER  -